📄 Statistical physics of vaccination
Zhen Wang, Chris T. Bauch, Samit Bhattacharyya, Alberto d'Onofrio, Piero Manfredi, Matjaz Perc,Nicola Perra, Marcel Salathé, Dawei Zhao
https://arxiv.org/pdf/1608.09010v3
📌 ABSTRACT
Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination - one of the most important preventive measures of modern times - is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.
Comments:150 pages, 42 figures; published in Physics ReportsSubjects:Physics and #Society (physics.soc-ph); #Statistical_Mechanics (cond-mat.stat-mech); Social and Information #Networks (cs.SI); #Populations and #Evolution (q-bio.PE); Applications (stat.AP)
Zhen Wang, Chris T. Bauch, Samit Bhattacharyya, Alberto d'Onofrio, Piero Manfredi, Matjaz Perc,Nicola Perra, Marcel Salathé, Dawei Zhao
https://arxiv.org/pdf/1608.09010v3
📌 ABSTRACT
Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination - one of the most important preventive measures of modern times - is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.
Comments:150 pages, 42 figures; published in Physics ReportsSubjects:Physics and #Society (physics.soc-ph); #Statistical_Mechanics (cond-mat.stat-mech); Social and Information #Networks (cs.SI); #Populations and #Evolution (q-bio.PE); Applications (stat.AP)
🎯 2017 : WHAT SCIENTIFIC TERM OR CONCEPT OUGHT TO BE MORE WIDELY KNOWN?
https://www.edge.org/response-detail/27036
#networks
https://www.edge.org/response-detail/27036
#networks
🌀 Applied Social Network Analysis in Python
https://www.coursera.org/learn/python-social-network-analysis
📌 About this course:
This course will introduce the learner to network modelling through the #networkx toolset. Used to model knowledge graphs and physical and virtual #networks, the lens will be social network analysis. The course begins with an understanding of what network modelling is (#graph_theory) and motivations for why we might model phenomena as networks. The second week introduces the networkx library and discusses how to build and #visualize networks. The third week will describe #metrics as they relate to the networks and demonstrate how these metrics can be applied to graph structures. The final week will explore the #social networking analysis workflow, from problem identification through to generation of insight.
https://www.coursera.org/learn/python-social-network-analysis
📌 About this course:
This course will introduce the learner to network modelling through the #networkx toolset. Used to model knowledge graphs and physical and virtual #networks, the lens will be social network analysis. The course begins with an understanding of what network modelling is (#graph_theory) and motivations for why we might model phenomena as networks. The second week introduces the networkx library and discusses how to build and #visualize networks. The third week will describe #metrics as they relate to the networks and demonstrate how these metrics can be applied to graph structures. The final week will explore the #social networking analysis workflow, from problem identification through to generation of insight.
#Review_Article on #granular_matter & #networks
🔖 Network Analysis of Particles and Grains
Lia Papadopoulos, Mason A. Porter, Karen E. Daniels, Danielle S. Bassett
🔗 https://arxiv.org/pdf/1708.08080
📌 ABSTRACT
The arrangements of particles and forces in granular materials and particulate matter have a complex organization on multiple spatial scales that range from local structures to mesoscale and system-wide ones. This multiscale organization can affect how a material responds or reconfigures when exposed to external perturbations or loading. The theoretical study of particle-level, force-chain, domain, and bulk properties requires the development and application of appropriate mathematical, statistical, physical, and computational frameworks. Traditionally, granular materials have been investigated using particulate or continuum models, each of which tends to be implicitly agnostic to multiscale organization. Recently, tools from network science have emerged as powerful approaches for probing and characterizing heterogeneous architectures in complex systems, and a diverse set of methods have yielded fascinating insights into granular materials. In this paper, we review work on network-based approaches to studying granular materials (and particulate matter more generally) and explore the potential of such frameworks to provide a useful description of these materials and to enhance understanding of the underlying physics. We also outline a few open questions and highlight particularly promising future directions in the analysis and design of granular materials and other particulate matter.
🔖 Network Analysis of Particles and Grains
Lia Papadopoulos, Mason A. Porter, Karen E. Daniels, Danielle S. Bassett
🔗 https://arxiv.org/pdf/1708.08080
📌 ABSTRACT
The arrangements of particles and forces in granular materials and particulate matter have a complex organization on multiple spatial scales that range from local structures to mesoscale and system-wide ones. This multiscale organization can affect how a material responds or reconfigures when exposed to external perturbations or loading. The theoretical study of particle-level, force-chain, domain, and bulk properties requires the development and application of appropriate mathematical, statistical, physical, and computational frameworks. Traditionally, granular materials have been investigated using particulate or continuum models, each of which tends to be implicitly agnostic to multiscale organization. Recently, tools from network science have emerged as powerful approaches for probing and characterizing heterogeneous architectures in complex systems, and a diverse set of methods have yielded fascinating insights into granular materials. In this paper, we review work on network-based approaches to studying granular materials (and particulate matter more generally) and explore the potential of such frameworks to provide a useful description of these materials and to enhance understanding of the underlying physics. We also outline a few open questions and highlight particularly promising future directions in the analysis and design of granular materials and other particulate matter.
#Review_Article on #granular_matter & #networks
🔖 Network Analysis of Particles and Grains
🔗 https://arxiv.org/pdf/1708.08080
🔖 Network Analysis of Particles and Grains
🔗 https://arxiv.org/pdf/1708.08080
🕸 New Course Alert! 💡 Learn about #networks (like this co-authorship network map of physicians publishing on hepatitis C) from both a computer science & an #economics standpoint! https://t.co/yZfOaX65yK
MIT OpenCourseWare
Networks
This course will highlight common principles that permeate the functioning of networks and how the same issues related to robustness, fragility and interlinkages arise in several different types of networks. It will both introduce conceptual tools from dynamical…
Working on #ComplexSystems? #networks & #data? Looking for applying your next methods on #Brain #Life #Disease #SocialSystems #Epidemics #HumanMobility? Aiming at working in a leading Italian research center?
Then our Lab can be your next stop! #MSCA 2019
Get in touch for info!
https://ec.europa.eu/research/mariecurieactions/news/2019-msca-call-individual-fellowships-open_en
Then our Lab can be your next stop! #MSCA 2019
Get in touch for info!
https://ec.europa.eu/research/mariecurieactions/news/2019-msca-call-individual-fellowships-open_en
🚼 Who Is the Most Important Character in Frozen? This article is a fantastic way for #kids to learn about #networks. Big ideas, yet readily understandable by young people. All they need is arithmetic and curiosity.
https://t.co/Qbi4pvnqj6
https://t.co/Qbi4pvnqj6
💰 Looking for a #Postdoc #complex #networks: exciting new @ERC_Research LINKS project on #network and #complexity to study finance to mitigate #ClimateChange. Join my new team at UCl_ISR https://t.co/YOM3zl6Dll. Deadline: Oct 11
UCL Institute for Sustainable Resources
We're hiring: Research Fellow/Senior Research Fellow in Complex Networks
Work at the UCL Institute for Sustainable Resources as a Research Fellow/ Senior Research Fellow in Complex Networks.
Focus on #Multilayer #Networks edited for the New Journal of Physics @IOPscience. 23 outstanding papers were collected, check them at https://t.co/ndtwJgTVdu.
Why, even during lockdown, do #coronavirus infection curves continue to grow linearly? The answer lies in networks.
"For any given #transmission rate there exists a critical degree of contact #networks below which linear #infection curves must occur and above which the classical S-shaped curves appear that are known from epidemiological models."
https://t.co/j70KwyijkW
"For any given #transmission rate there exists a critical degree of contact #networks below which linear #infection curves must occur and above which the classical S-shaped curves appear that are known from epidemiological models."
https://t.co/j70KwyijkW